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Alternating Scheme for Supervised Parameter Learning with Application to Image Segmentation

  • Lucas Franek
  • Xiaoyi Jiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6854)

Abstract

This paper presents a novel alternating scheme for supervised parameter learning. While in previous methods parameters were optimized simultaneously, we propose to optimize parameters in an alternating way. In doing so the computational amount is reduced significantly. The method is applied to four image segmentation algorithms and compared with exhaustive search and a coarse-to-fine approach. The results show the efficiency of the proposed scheme.

Keywords

Image Segmentation Exhaustive Search Segmentation Algorithm Normalize Mutual Information Computational Amount 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lucas Franek
    • 1
  • Xiaoyi Jiang
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of MünsterMünsterGermany

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